Accelerating numerical simulations using machine learning.

Fast numerical simulations can be achieved through parameter optimization using machine learning!

In the analysis of products dealing with fluids, structures, electromagnetic fields, etc., through numerical simulations of partial differential equations, a significant challenge is the long computation time. By using machine learning techniques, it is possible to significantly reduce computation time. Additionally, by executing simulations that have traditionally been performed on CPUs on GPUs, a speedup of up to 100 times can be achieved in the best case. 【Technical Details】 ■ Development of Faiss's machine learning-friendly numerical simulation algorithm for acceleration - By appropriately setting several parameters of the numerical simulation algorithms that have been traditionally used, we have developed an algorithm that can accelerate without compromising the conventional accuracy. - Fast numerical simulations can be realized through parameter optimization using machine learning. ■ Execution of numerical simulations on GPUs - By executing simulations that have traditionally been performed on CPUs on GPUs, a speedup of up to 100 times can be achieved in the best case. *For more details, please download the PDF or feel free to contact us.

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